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THE ML ENGINEER 🤖 Issue #109   This week in Issue #109:   Production ML Monitoring at SF 100+ Free (& OSS) Dev Books The geopolitics of AI & ML Ignoring Advice & Building a Decent Software Startup Uber’s Real-Time Push Platform Featured OSS Production ML Libraries Awesome AI Guidelines to check out this week + more 🚀   Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!   If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!       Production ML Monitoring at SF We'll be speaking at the SF Big Analytics meetup on "Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance" in February. It will be happening online so feel free to join even if you're not in the area - we will be joining from across the pond!       100+ Free (& OSS) Dev Books Following the DS & ML books list, the team at "InsaneApp" has put together a fantastic 100+ free programming books ranging across a broad set of programming languages (Python, C++, C, Java and beyond).       The geopolitics of AI & ML Entrepreneur First CEO Matt Clifford released a podcast conversation with State-of-AI report author Co-author Ian Hogarth to discuss the geopolitics of AI, where they explore how technology collides with politics, culture and society.       Building Decent Software Startup "How to Ignore Most Startup Advice and Build a Decent Software Business" by SpaCy and Explosion.ai Co-founder Ines Montani. In this talk Ines shares some of the things she has learned from building a successful software company around commercial developer tools and their open-source library spaCy.       Uber’s Real-Time Push Platform Uber builds multi-sided marketplaces handling millions of trips every day across the globe. This has presented challenges that have required innovative thinking around the interaction between the multiple heterogeneous types of applications that require data flow. In this post they discuss the challenges with polling, and showcase how they introduced a push infrastructure through their framework RAMEN.       OSS: Metadata Management   The topic for this week's featured production machine learning libraries is Metadata Management. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are: Amundsen - Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data. DataHub - DataHub is LinkedIn's generalized metadata search & discovery tool. Metacat - Metacat is a unified metadata exploration API service. Metacat focusses on solving these three problems: 1) Federate views of metadata systems. 2) Allow arbitrary metadata storage about data sets. 3) Metadata discovery. ML Metadata - a library for recording and retrieving metadata associated with ML developer and data scientist workflows. Also TensorFlow ML Metadata.   If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request!        OSS: Awesome AI Guidelines   As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:   AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018 From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices   If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!        © 2018 The Institute for Ethical AI & Machine Learning